{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook covers the basics of creating TransferFunction object, obtaining time and energy resolved responses, plotting them and using IO methods available. Finally, artificial responses are introduced which provide a way for quick testing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up some useful libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import relevant stingray libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from stingray.simulator.transfer import TransferFunction\n",
    "from stingray.simulator.transfer import simple_ir, relativistic_ir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating TransferFunction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A transfer function can be initialized by passing a 2-d array containing time across the first dimension and energy across the second. For example, if the 2-d array is defined by `arr`, then `arr[1][5]` defines a time of 5 units and energy of 1 unit.\n",
    "\n",
    "For the purpose of this tutorial, we have stored a 2-d array in a text file named `intensity.txt`. The script to generate this file is explained in `Data Preparation` notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "response = np.loadtxt('intensity.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize transfer function by passing the array defined above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(524, 744)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transfer = TransferFunction(response)\n",
    "transfer.data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, time and energy spacing across both axes are set to 1. However, they can be changed by supplying additional parameters `dt` and `de`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Obtaining Time-Resolved Response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 2-d transfer function can be converted into a time-resolved/energy-averaged response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "transfer.time_response()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This sets `time` parameter which can be accessed by `transfer.time`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transfer.time[1:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, energy interval over which to average, can be specified by specifying `e0` and `e1` parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Obtaining Energy-Resolved Response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Energy-resolved/time-averaged response can be also be formed from 2-d transfer function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "transfer.energy_response()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This sets `energy` parameter which can be accessed by `transfer.energy`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transfer.energy[1:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Responses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TransferFunction() creates plots of `time-resolved`, `energy-resolved` and `2-d responses`. These plots can be saved by setting `save` parameter. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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U3O8h57eugzGJoU4fY7ZH779T30n6uwf/HcSf1//u9Zmfyf02Qd9O4h9y+djv/FRzPvhz\nb1INXFmE6Ja/47Z3/wU3HgyI3M1daCwdGTHc3TnxrMsN6MnCUMGycUsn0+MV+rf0cezxETKd3fSe\n3UGQyRL0D4lxLnWJDBFWEzmha1CMT60MG06HsSMwuFOM0eDpsj7MQs+QGJJ6RSSKriGYPArdm8QA\nN6rJhVfoEV3ce3xBRvYDyHfJxV3oFe+1NpV41QDFfjHu1opBz3U4D9V5YWEOWx2HbEdiJIIMNqpj\ncl3YRgVjctj6NCZTdAa2Jp5u1h0nqmFMSkrxxth1HsZLEN7oGv9+XYx4w+3jJYdMRoytN4jWyneV\nNt6QyBBhNjFCtgGEMw1/VBeDmnWeua07gzVLkvDSCYhxjretJ222uN8lcOuzKWPqjKLJJJ2Jl1bi\nuwDkP2Od5x2kPHDsTE84qss677lHkRh1L5vZWtKB+c+f7rjSQrQ/d+Q62iB9N0Py260Q8xSqajlq\nwNcR0dH7+duXXsVo1QAhobH0ZS0FJ292ZOCMzohyw9CRsYQGBnuybH3WxdTHx+isVshv3EzHWTnR\na7sHqD9yF8HGHXDkUSh2y4HOejYcexTGjsLQ2VAeh+6NzhsfkousbxtMDEPXpuTiqkyI19yoycWT\n7xRDmik4XTWS1wD5HrnIigNiaDBi6Cvj4pHnOp0XviGRLkyAMQab65R9ohom34OdPALZIiZbig2W\nbVQxYS4x3JVRiEIx2o0qJtvhDKqV9tUriVHId83sUHzn442RN9yZQur234ohazgjFISJnu31dmmY\nHCPtpPljZwqJ7GAj8AWUvIRio5RH69pq5L8Qyycg5/QSjyfuKMLkeL7d4OQMk3i1NhJjHnv/NnU+\nZp7fd0BB6Lz0lCGNDX5K247Pmf5O3PdgQpHejIF6NZF/0sdMyz/ehNlGopl7Tz5YOfPWrhKKGvB1\ngJ0+zo2vOI/vDMstqEGMc28e+nJQj6A7axkqWmoRXLK7l4mRafpP30r52FGqR49gMhlyAxswHb3Y\nyRMShIoaZC64Uk6ycQeMH5OL5vh+2HiWGOAwJ7JIJg+dGyQgWOgWr7g0AOOHnIddg+4tMDksF2a+\nB6rjzijVE23Za9PZkiyTR8Srzpbk4s91iLEPXNDSNpxHXcLkOqA2jcl3YaePgwmxUQ3TsVEkkJQm\nbHIdsSRiyyOYjo1yZxB7gM7TrFeBKLkTMIHcVWRy8l5tOjHMOK86CBNZAlzHVEqMiDfqGGZ48UEO\nbJjSrSMgcN5jJOfzRsg23Oe3yTlsAzJF9xmyiYSS6xA5y3cwmULq+HaW8Q1S+rbzXKOUtm2t6yQC\nOV+UClJ63TvW0/3ncN+Dl4hA/jdRLfUvNkknEzgZyzaSTCR/1+A7EK+FBxnXgZrUNu7z+P2CEKy/\n+/Aef5SKBZw8J1GNcFVRA97mRLf8He9667uo24BcIN52Tw6ygXgFOzsiqpHh7KE8R4+X2TFUpFap\n072xBxNm6Nh2GmFHFyZfEq87V8D0bpTn9VoilUwcgw07xBj3DMHUqDxCEuTL5OX96ROyz/QIFPsA\nC41ALsYgIwalXoae02DqmBzDpQnGt/pe9igNYDJFCe7lOlPebka869qk6NQmcMbWOOMdiJRhJFBn\nch3OU2xgGzVMvjM2hpILPEua8IYgk0tJIe5OIpMX45DJidGtTcqdQ3065Yl7/GdxkkHUSDqtwHmY\nJnDvAzjZIszK9x96zzgdwHOGDee5ei0dHzCMUp2FTe4WfMaH9+7DbPJ9xlJPPZEb4uwVpzN7+aZe\nTTznuP2prJB0IDF+7jqOdKcWyx42OY7viHznhp25ve8c/P9uRrB2VszBy0q+3V5y8t/XCmaotKkD\n3rbZMQpgJ47w7t9+F3UrXnc2gFIGGlZS/kBkk60lizGwcUORnq0bKfb1kO3tJ9vTS6Z/EyabTy7Y\nrgHRaHMlSWUDMdQ7LhHjXOx2J49gehw6BsUTDzISdGzUoHOjaNNY8Z5zndC5SV53bHQGvyAG3l+U\n3hOtTWI6N824yKyNMLkusFYkDhOIHBJkMPluaFSwcbpaDlMacMG9AFudSCQFF8w06Ys3Dox5A+5v\nzyvOwGRSGSpVMR4+UJoO/nmDGR/ba9SpoFps5LzMUE9u9+M0PhBjXU+kjDjNMOd09GKiDXsD5Y1a\n2ntu1JL1ftvYqEVP1t29tJJO1fMBxch9Dr+NlzPi7JN6qiOYtS5tYH3sAJt8b+nAqv89/B1K/LsE\nyT7+db0i2/iOF2be2XjSWTDpTmEFCUxzy1qjBrxNsWMH+evnX0g1MhRDMd49OcnF3lqyXL4h4hXP\n28zOM3qJLGTzGXKFDOXjI+QGN5E743xM7ybYfCa1kWHYuksOPD0OZ14mMsHg6ZK3XJ6A6pRIJaU+\nuThr02LYS4MwdkiyPUr9Ip1UxuUCy5bE2FUnZzY+1ynasd8mzMmtv0uJs5XxmR4XYKNafKtssh3Y\n+JjGpeBlsLaBrZedNFKSd4v9iRfoDEScQZLWlb2x89p1Jk+SgWEk+Bhm3XsFMUT1cpKHjUk8ap8l\nYcK4I5mRtgjOcGeS5z5gmc7iCEKXqhgmhi/MuM5R7ixo+EBcyouFpFPMermEVMZGIHcP3iB6+QES\nOcuYJNgahEmHMcPXTBnXMJto4dYmOekzPlvaa3ffmdeqfVDTG98wmwQZTTizE40DoOkURZP8BnGA\nmJnetu/oguyKyickZ190WWtUQmlDom+8i0+/82+ZqAf05Ww8qGYgb7lkS4ZHj9UIQ8OxR48wPl5j\n5/lbsPU6ma5u8lt2Ek2MEh15jGBwK5w4THbnbjnw4DZ5rFWg0wUbe7fIusqkaNv1ilxcWy+U9ROH\nxagXehIvKVuUCzjfLcbb52I3quKB24as91qsD26FeZe2lxFpxMkfSV6wk1UyObkYjMFWJ11qn5FR\nmiDtDnOSURIPcsmKxBGEmEJPEqAzjSRAV5tO5B0/aCU9iMSnGDZq0uF4A+KNuU/hazg5Iqq5QJvb\nP9fhDFxdtquMOwPtUvDqLgc9bWhtI5ENGtXEYHpN2pDSp1PShc/vTudrx0FJJ9Nk8k6rdgYv8kbR\nHSMwiRQRH9N7y5F0QD6oGncEYeKl28jJNul9nWTidX8skPqOAZ8tlBhZZ3jDHDQayXeSzmOPBwa5\nu4Ugk9wVRfWkE/ASzQoP8NEgptIUtjbNX7zt74gI6M6K8c6563h7hyUIA56+ux+CENuoky9WiGo1\ncn0DZLq6sdPjBMUOKHbJSMcN28VgRw2X7mUlW6RekcySYm9yQZbH3YhA5y1PHIGNu2HscTFgxX65\ncEobZNBMdUK87ckj0LNDBr9UJ1JerPOYyieg2CeGu17G1muYYj+2Mi7ZIMZga9MuH7uGaQTY6WOY\n0gZMpoCtTsjAF6c323oFE2YxtpEEzoIs2LJo1kRggyRfPK1TB5nUiMVyEnz0erM33lEt6WACFyhM\nB9Pi/GqT6N4+AGgyyXcTSw0pKaM+LefwHmu9Kp1MOuCbfu49dLzxSud6O3yAMnRBP+8R2yg5xuxM\nGP85alOuY6vIObPFxEufocO7/dM6dZgjHrzkZQxIjKmPe/i7Fm/o/SAtL/XEv0822W52Dns8iCjl\noftH76V7rT/dGa0A7ZpGqBJKm/G5a3cSuUyTDnfNZ4ylK2vZeVoXX3+4xme/d4K77h4m09lNR38X\npW07qRw9TJArUJ8Yh4IYRUpdcGgv7LhYDrRlt6wvdicXc+eQG3rtZIUwK4HJbEkurNq0M+ipW+KJ\nQ1AZg45NYgTzPTA1nEgq1g2IqZcT3TpTAAwmW3KyRzJIw9Yrsn2uM+Xt+RzxEFPoTd3GV102yhRk\nCljbgGwBO3WMeECI11i9ph3VE5mnOpkYlUxe2ghODvHDz8vu7sEFZutuaDmBHNN7ymEmkRNiT9Zr\n0c5DbVSSc3ivNfAZJM4QxWlxqSCdMdIZ+DuFGQbJJJJLHIz0unc10bnTBtQfo15J9k2PckwPkIq9\n4VpinMEZfptqd0oTB2akCPrgY1qzhyQQ6j+fCZJRsD4YCzPvDNLGOx3UbFSJg6/xCNNUfvoKFjBp\nVw1cPfA2IvryH3P3iKEnZ8kHIpmc223JZw1hRnKgX3ZhB52bN9GYnqKwaQuN6Ukqw09QGNpKbeQo\n2V2XQEevDGm3Fs68BA78BHo3izEu9YoXlytB3044eIdkpPiLGyAzDljJ8Y7qSU0RLFTGMD3bseUT\ncrE7D1q8+LwUyQRsZQxTGhQ9OtcpHnamgK2OizGPg43uQo4y2PqYGOd6GVPokwu27kf5OY3TG6ds\nCWwUZ5+YTH6W1+pusW2UjFQ0zsutThIPXsm4EaL16UTC8ZIJyHa5DuL8Zt+R+EEw2ZJ0Zpmi+/5S\n2zScbOTbHdWdRFOX7602lWjxxkA9nb9tkrYFoWQE+hon8QAf12F5Kcan+tWrrq5KkEgQcQDTY2el\nE2blriFbSqXnZVIeswu6eonNG1W/byyZRMn3BklnGq9zEou1SSzA/2b+bsgyMxhsAjlu3FmlMmbi\nlMKU8Q4yxOmPK0SbOuBr4oGHSK3bL7nX/ciszA8AXwd6U9u+A3gQmfDzhWvQtrbBWsvn/voDBAaK\nIfTlLb056OrK0tFTYMOOQaYnqgy84pfIP++V5PoHKR8+SFStUthyGpnObkwYJkO5swWRTLacLxfq\n4YdkOe1SuQXv3gQnHoPBM6FvhxiUriFZ8l2JVpvJi0eeKcjrjg1SqwNg6qgEE61NZAenU5t8t0gf\nac/SGBlU4/KKbVVmlhIZxWWe1MvO6KXS6tJeVdRIOpo4t9x5mJVxMQC+JgkmGdnph4JPHU887SAL\nUTXlqUby2mv93qC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Ez76DRWZrSeUb+5lv/IhCiDMcrNPybW3KzZkZYitT0u56EgQ0GXfn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\n+ronjZrkhter2OljMku8CaB3CCrTUJ3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2bLUllE8C3wHOAfYDvwS8G3gBEn19vnsNEp39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\nkiRJkiRJkiRJW8oficN/jVry+kIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b81f7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "transfer.plot(response='2d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GjIFPP7XO53nz4J13rON55kzrcP77311HKBIeriuE14H9wB+BRxzG4oSajDLj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\niIiIiIiIiIiIiIiIiEP/H7DvjyvIf8HIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11138c4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "transfer.plot(response='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zM4/ed5+NBzzzjB1BPH48dO9ug8VSGlq3hmbNYMMGaNs27tIIxHekcnPgDiwU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\nERERERERERERERERERERERERKab/B5WvRS57HCkNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11113bfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "transfer.plot(response='energy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By enabling `save=True` parameter, the plots can be also saved."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TransferFunction can be saved in pickle format and retrieved later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "transfer.write('transfer.pickle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saved files can be read using static `read()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transfer_new = TransferFunction.read('transfer.pickle')\n",
    "transfer_new.time[1:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Artificial Responses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For quick testing, two helper impulse response models are provided."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1- Simple IR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "simple_ir() allows to define an impulse response of constant height. It takes in time resolution starting time, width and intensity as arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x112d48990>]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b828bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s_ir = simple_ir(dt=0.125, start=10, width=5, intensity=0.1)\n",
    "plt.plot(s_ir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2- Relativistic IR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A more realistic impulse response mimicking black hole dynamics can be created using relativistic_ir(). Its arguments are: time_resolution, primary peak time, secondary peak time, end time, primary peak value, secondary peak value, rise slope and decay slope. These paramaters are set to appropriate values by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10cca92d0>]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vwj4/24HH4s9lVGe2rz8S5rV2KwlW/c2Ap4H/xP6jJwpKrRXYB70rMAwbCScK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111313110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r_ir = relativistic_ir(dt=0.125)\n",
    "plt.plot(r_ir)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
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